Approximate planning with hierarchical partially observable Markov decision process models for robot navigation

We propose and investigate a planning framework based on the hierarchical partially observable Markov decision process model (HPOMDP), and apply it to robot navigation. We show how this framework can be used to produce more robust plans as compared to flat models such as partially observable Markov decision processes (POMDPs). In our approach the environment is modeled at different levels of resolution, where abstract states represent both spatial and temporal abstraction. We test our hierarchical POMDP approach using a large simulated and real navigation environment. The results show that the robot is more successful in navigating to goals starting with no positional knowledge (uniform initial belief state distribution) using the hierarchical POMDP framework as compared to the flat POMDP approach.